Autors: Marev, K., Georgiev, K. K.
Title: Automated aviation occurrences categorization
Keywords: aviation safety, occurrence reporting, NLP, NASA ASRS, fastai, neural networks

Abstract: Information about aviation safety events is collected by all participants in the aviation system. Reporting and initial assessment usually involves assigning categories from a predefined nomenclature (scheme) aligned with the purpose of the reporting system and the established processing practices. Such manual categorization is time and resource consuming and, more importantly, limiting the application of the dataset. We apply and evaluate the effectiveness of a state of the art Neural Networks based algorithm for Natural Language Processing for classification of aviation safety report narratives. Multi-class, multi-label supervised learning is performed on two small datasets, 4500 and 8000 cases with 16 and 54 classes respectively, both extracted from the NASA Aviation Safety Reporting System. The results are promising if compared to recent studies and considering that an off the shelf algorithm without much customization is applied.



    2019 International Conference on Military Technologies (ICMT), pp. 5, 2019, Czech Republic, IEEE, ISBN 978-1-7281-4593-8

    Copyright IEEE

    Цитирания (Citation/s):
    1. Syed, K., Sleeman, W., Hagan, M., Palta, J., Kapoor, R., & Ghosh, P. (2020). Automatic Incident Triage in Radiation Oncology Incident Learning System. Healthcare, 8(3), 272. doi:10.3390/healthcare8030272 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Amin, Nadine, Tracy Yother, Mary Johnson, and Julia Rayz. "Exploration of Natural Language Processing (NLP) Applications in Aviation." The Collegiate Aviation Review International 40, no. 1 (2022). - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus